# coding:utf-8
import input_data
import tensorflow as tf

mnist = input_data.read_data_sets('MNIST_data', one_hot=True)

x = tf.placeholder('float32',[None, 784])

w = tf.Variable(tf.zeros([784,10]))

b = tf.Variable(tf.ones([10]))

y = tf.nn.softmax(tf.matmul(x,w) + b)

# 比较粗糙的理解是，交叉熵是用来衡量我们的预测用于描述真相的低效性
y_ = tf.placeholder("float", [None,10])

cross_entropy = -tf.reduce_sum(y_*tf.log(y))

train_step = tf.train.GradientDescentOptimizer(learning_rate=0.01).minimize(cross_entropy)

init = tf.initialize_all_variables()

sess = tf.Session()

sess.run(init)

for i in range(1000):
    batch_xs,batch_ys = mnist.train.next_batch(100)
    sess.run(train_step,feed_dict={
        x:batch_xs,
        y_:batch_ys
    })

correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))

accuracy = tf.reduce_mean(tf.cast(correct_prediction,'float'))

print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))


